Predictive control has recently received much attention from researchers. However a challenging problem to be solved is how to tune the parameters of the predictive controller. So far, only few guidelines related to t...Predictive control has recently received much attention from researchers. However a challenging problem to be solved is how to tune the parameters of the predictive controller. So far, only few guidelines related to tuning of the parameters of predictive controllers have been provided in literature. In practice, these parameters are generally off-line determined by the designers' experience. From the point of view of process control, it is difficult to find out the optimal parameters for the control system based on a single quadratic performance index, which is used in the standard predictive control algorithm. The fuzzy decision-making function is investigated in this paper. Firstly, M control actions are achieved by unconstrained predictive control algorithm, and fuzzy goals and fuzzy constraints are then calculated and the global satisfaction degree is obtained by fuzzy inference. Moreover, the weighting coefficient λ in the cost function is tuned using simulation optimization according to the fuzzy criteria.展开更多
In autonomous driving,an unprotected left turn is a highly challenging scenario.It refers to the situation where there is no dedicated traffic signal controlling the left turns;instead,left-turning vehicles rely on th...In autonomous driving,an unprotected left turn is a highly challenging scenario.It refers to the situation where there is no dedicated traffic signal controlling the left turns;instead,left-turning vehicles rely on the same traffic signal as the through traffic.This presents a significant challenge,as left-turning vehicles may encounter oncoming traffic with high speeds and pedestrians crossing against red lights.To address this issue,we propose a Model Predictive Control(MPC)framework to predict high-quality future trajectories.In particular,we have adopted the infinity norm to describe the obstacle avoidance for rectangular vehicles.The high degree of non-convexity due to coupling terms in our model makes its optimization challenging.Our way to solve it is to employ Sequential Convex Optimization(SCP)to approximate the original non-convex problem near certain initial solutions.Our method performs well in the comparison with the widely used sampling-based planning methods.展开更多
This paper investigates the use of fuzzy decision making in predictive control. The use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than ...This paper investigates the use of fuzzy decision making in predictive control. The use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than the usual weighting sum of squared errors. Compared to the standard quadratic objective function, with the fuzzy decision-making approach, the designer has more freedom in specifying the desired process behavior.展开更多
Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. ...Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to reduce the model errors caused by changes of the process under control. To cope with the difficult problem of nonlinear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.展开更多
In order to solve the problem of weighting factors selection in the conventional finite-control-set model predictive control for a grid-connected three-level inverter,an improved multi-objective model predictive contr...In order to solve the problem of weighting factors selection in the conventional finite-control-set model predictive control for a grid-connected three-level inverter,an improved multi-objective model predictive control without weighting factors based on hierarchical optimization is proposed.Four control objectives are considered in this strategy.The grid current and neutral-point voltage of the DC-link are taken as the objectives in the first optimization hierarchy,and by using fuzzy satisfaction decision,several feasible candidates of voltage vectors are determined.Then,the average switching frequency and common-mode voltage are optimized in the second hierarchy.The average ranking criterion is introduced to sort the objective functions,and the best voltage vector is obtained to realize the coordinated control of multiple objectives.At last,the effectiveness of the proposed strategy is verified by simulation results.展开更多
The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimi...The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production.This objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding rate.This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming.The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems.Fish farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters.Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption.To evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried out.The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.展开更多
This paper aims at using of an approach integrating the fuzzy logic strategy for hypoxemic hypoxia tissue blood carbon dioxide human optimal control problem. To test the efficiency of this strategy, the authors propos...This paper aims at using of an approach integrating the fuzzy logic strategy for hypoxemic hypoxia tissue blood carbon dioxide human optimal control problem. To test the efficiency of this strategy, the authors propose a numerical comparison with the direct method by taking the values of determinant parameters of cardiovascular-respiratory system for a 30 years old woman in jogging as her regular physical activity. The results are in good agreement with experimental data.展开更多
The grey fuzzy variable was defined for the two fold uncertain parameters combining grey and fuzziness factors. On the basis of the credibility and chance measure of grey fuzzy variables, the distribution center inven...The grey fuzzy variable was defined for the two fold uncertain parameters combining grey and fuzziness factors. On the basis of the credibility and chance measure of grey fuzzy variables, the distribution center inventory uncertain programming model was presented. The grey fuzzy simulation technology can generate input-output data for the uncertain functions. The neural network trained from the inputoutput data can approximate the uncertain functions. The designed hybrid intelligent algorithm by embedding the trained neural network into genetic algorithm can optimize the general grey fuzzy programming problems. Finally, one numerical example is provided to illustrate the effectiveness of the model and the hybrid intelligent algorithm.展开更多
We aimed in this paper to use fuzzy logic approach to solve a hepatitis B virus optimal control problem. The approach efficiency is tested through a numerical comparison with the direct method by taking the values of ...We aimed in this paper to use fuzzy logic approach to solve a hepatitis B virus optimal control problem. The approach efficiency is tested through a numerical comparison with the direct method by taking the values of determinant parameters of this disease for people administrating the drugs. Final results of both numerical methods are in good agreement with experimental data.展开更多
This paper aims at the development of an approach integrating the fuzzy logic strategy for a glucose and insulin in diabetic human optimal control problem. To test the efficiency of this strategy, the author proposes ...This paper aims at the development of an approach integrating the fuzzy logic strategy for a glucose and insulin in diabetic human optimal control problem. To test the efficiency of this strategy, the author proposes a numerical comparison with the indirect method. The results are in good agreement with experimental data.展开更多
This paper aims at the development of an approach integrating the fuzzy logic strategy for a therapeutic hepatitis C virus dynamics optimal control problem. To test the efficiency of this strategy, the authors propose...This paper aims at the development of an approach integrating the fuzzy logic strategy for a therapeutic hepatitis C virus dynamics optimal control problem. To test the efficiency of this strategy, the authors propose a numerical comparison with the direct method by taking the values of determinant parameters of this disease for people administrating the drugs. The results are in good agreement with experimental data.展开更多
Real time multi step prediction of BP network based on dynamical compensation of system characteristics is suggested by introducing the first and second derivatives of the system and network outputs into the network i...Real time multi step prediction of BP network based on dynamical compensation of system characteristics is suggested by introducing the first and second derivatives of the system and network outputs into the network input layer, and real time multi step prediction control is proposed for the BP network with delay on the basis of the results of real time multi step prediction, to achieve the simulation of real time fuzzy control of the delayed time system.展开更多
To visually describe the sanding pattern,this study constructs a new particle-scale microstructure model of weakly consolidated formation,and develop the corresponding methodology to simulate the sanding process and p...To visually describe the sanding pattern,this study constructs a new particle-scale microstructure model of weakly consolidated formation,and develop the corresponding methodology to simulate the sanding process and predict sand cavity shape.The microstructure model is a particle-objective model,which focuses on the random sedimentation of every sand grain.In the microstructure,every particle has its own size,sphericity and inclination angle.It is used to simulate the actual structure of cemented granular materials,which considers the heterogeneity and randomness of reservoir properties,provides the initial status for subsequent sanding simulation.With the particle detachment criteria,the microscopic simulation of sanding can be visually implemented to investigate the pattern and cavity shapes caused by sand production.The results indicate that sanding always starts initially from the borehole border,and then extends along the weakly consolidated plane,showing obvious characteristic of randomness.Three typical microscopic sanding patterns,concerning pore liquefaction,pseudo wormhole and continuous collapse,are proposed to illustrate the sanding mechanism in weakly consolidated reservoirs.The nonuniformity of sanding performance depends on the heterogeneous distribution of reservoir properties,such as rock strength and particle size.Finally,the three sanding patterns are verified by visually experimental work.The proposed integrated methodology is capable of predicting and describing the sanding cavity shape of an oil well after long-term sanding production,and providing the focus objective of future sand control measure.展开更多
Efficient built environment control is essential for balancing energy consumption,thermal comfort,and indoor air quality(IAQ),especially in spaces with highly dynamic and intermittent occupancy patterns.Traditional co...Efficient built environment control is essential for balancing energy consumption,thermal comfort,and indoor air quality(IAQ),especially in spaces with highly dynamic and intermittent occupancy patterns.Traditional control strategies,such as fixed schedules or simple occupancy-based rules,often fail to address the stochastic nature of occupancy behaviors,leading to suboptimal performance.This study proposes a stochastic occupancy-integrated model predictive control(MPC)strategy that advances built environment optimization through several innovative contributions.First,the proposed MPC integrates stochastic occupancy number predictions into its control scheme,enabling multi-objective optimization considering thermal comfort and IAQ for spaces with sudden occupancy changes and irregular usage.Second,the stochastic differential equations(SDE)-based building dynamic models are developed considering the stochasticity and time-inhomogeneity of occupancy heat gains and CO_(2)generations in the prediction of indoor temperature,CO_(2)concentration and energy consumption.Third,a TRNSYS-Python co-simulation platform is established to evaluate the MPC strategy’s performance,addressing the discrepancies between the SDE models used for MPC and the actual process of the target system.Finally,the study comprehensively evaluates the MPC’s multi-dimensional performance under different optimization weight combinations and benchmarks it against two baseline strategies:a fixed-schedule(FIX)strategy and occupancy-based control(OBC)strategies with varying per-person fresh airflow rates.Simulation results demonstrate that the proposed MPC achieves 32%energy savings and 17%IAQ improvement compared to the FIX strategy,and 30%thermal comfort improvement and 20%IAQ improvement with the same energy consumption compared to OBC.These findings highlight the robustness and enhanced performance of the proposed MPC in addressing the complexities of stochastic and time-varying occupancy,offering a state-of-the-art solution for energy-efficient and occupant-centric built environment control.展开更多
文摘Predictive control has recently received much attention from researchers. However a challenging problem to be solved is how to tune the parameters of the predictive controller. So far, only few guidelines related to tuning of the parameters of predictive controllers have been provided in literature. In practice, these parameters are generally off-line determined by the designers' experience. From the point of view of process control, it is difficult to find out the optimal parameters for the control system based on a single quadratic performance index, which is used in the standard predictive control algorithm. The fuzzy decision-making function is investigated in this paper. Firstly, M control actions are achieved by unconstrained predictive control algorithm, and fuzzy goals and fuzzy constraints are then calculated and the global satisfaction degree is obtained by fuzzy inference. Moreover, the weighting coefficient λ in the cost function is tuned using simulation optimization according to the fuzzy criteria.
基金supported by the National Natural Science Foundation of China under Grant no.62373059the Beijing Institute of Technology Research Fund Program for Young Scholars.
文摘In autonomous driving,an unprotected left turn is a highly challenging scenario.It refers to the situation where there is no dedicated traffic signal controlling the left turns;instead,left-turning vehicles rely on the same traffic signal as the through traffic.This presents a significant challenge,as left-turning vehicles may encounter oncoming traffic with high speeds and pedestrians crossing against red lights.To address this issue,we propose a Model Predictive Control(MPC)framework to predict high-quality future trajectories.In particular,we have adopted the infinity norm to describe the obstacle avoidance for rectangular vehicles.The high degree of non-convexity due to coupling terms in our model makes its optimization challenging.Our way to solve it is to employ Sequential Convex Optimization(SCP)to approximate the original non-convex problem near certain initial solutions.Our method performs well in the comparison with the widely used sampling-based planning methods.
基金This project was supported by the National Nature Science Foundation of China (No. 60074004) andHebei Provincial Natural Scien
文摘This paper investigates the use of fuzzy decision making in predictive control. The use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than the usual weighting sum of squared errors. Compared to the standard quadratic objective function, with the fuzzy decision-making approach, the designer has more freedom in specifying the desired process behavior.
基金Sponsored by the National Electric Power Corporation Foundation of China(Grant No.SPKJ010-27)
文摘Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to reduce the model errors caused by changes of the process under control. To cope with the difficult problem of nonlinear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.
基金Supported by the Key Research and Development Program of Hunan Province of China(2018GK2031)the Independent Research Project of State Key Laboratory of Advance Design and Manufacturing for Vehicle Body(71965005)+2 种基金the Innovative Construction Program of Hunan Province of China(2019RS1016)the 111 Project of China(B17016)the Excellent Innovation Youth Program of Changsha of China(KQ2009037).
文摘In order to solve the problem of weighting factors selection in the conventional finite-control-set model predictive control for a grid-connected three-level inverter,an improved multi-objective model predictive control without weighting factors based on hierarchical optimization is proposed.Four control objectives are considered in this strategy.The grid current and neutral-point voltage of the DC-link are taken as the objectives in the first optimization hierarchy,and by using fuzzy satisfaction decision,several feasible candidates of voltage vectors are determined.Then,the average switching frequency and common-mode voltage are optimized in the second hierarchy.The average ranking criterion is introduced to sort the objective functions,and the best voltage vector is obtained to realize the coordinated control of multiple objectives.At last,the effectiveness of the proposed strategy is verified by simulation results.
基金funded by the Ministry of Science,ICT CMC,202327(2019M3F2A1073387)this work was supported by the Institute for Information&communications Technology Promotion(IITP)(NO.2022-0-00980,Cooperative Intelligence Framework of Scene Perception for Autonomous IoT Device).
文摘The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production.This objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding rate.This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming.The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems.Fish farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters.Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption.To evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried out.The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.
文摘This paper aims at using of an approach integrating the fuzzy logic strategy for hypoxemic hypoxia tissue blood carbon dioxide human optimal control problem. To test the efficiency of this strategy, the authors propose a numerical comparison with the direct method by taking the values of determinant parameters of cardiovascular-respiratory system for a 30 years old woman in jogging as her regular physical activity. The results are in good agreement with experimental data.
基金Supported bythe Science and Research Foundationof Shanghai Municipal Educational Commssion (05DZ33)
文摘The grey fuzzy variable was defined for the two fold uncertain parameters combining grey and fuzziness factors. On the basis of the credibility and chance measure of grey fuzzy variables, the distribution center inventory uncertain programming model was presented. The grey fuzzy simulation technology can generate input-output data for the uncertain functions. The neural network trained from the inputoutput data can approximate the uncertain functions. The designed hybrid intelligent algorithm by embedding the trained neural network into genetic algorithm can optimize the general grey fuzzy programming problems. Finally, one numerical example is provided to illustrate the effectiveness of the model and the hybrid intelligent algorithm.
文摘We aimed in this paper to use fuzzy logic approach to solve a hepatitis B virus optimal control problem. The approach efficiency is tested through a numerical comparison with the direct method by taking the values of determinant parameters of this disease for people administrating the drugs. Final results of both numerical methods are in good agreement with experimental data.
文摘This paper aims at the development of an approach integrating the fuzzy logic strategy for a glucose and insulin in diabetic human optimal control problem. To test the efficiency of this strategy, the author proposes a numerical comparison with the indirect method. The results are in good agreement with experimental data.
文摘This paper aims at the development of an approach integrating the fuzzy logic strategy for a therapeutic hepatitis C virus dynamics optimal control problem. To test the efficiency of this strategy, the authors propose a numerical comparison with the direct method by taking the values of determinant parameters of this disease for people administrating the drugs. The results are in good agreement with experimental data.
文摘Real time multi step prediction of BP network based on dynamical compensation of system characteristics is suggested by introducing the first and second derivatives of the system and network outputs into the network input layer, and real time multi step prediction control is proposed for the BP network with delay on the basis of the results of real time multi step prediction, to achieve the simulation of real time fuzzy control of the delayed time system.
基金financially supported by the National Natural Science Foundation of China(Grant No.51774307,52074331,42002182)partially supported by Major Special Projects of CNPC,China(ZD2019-184)。
文摘To visually describe the sanding pattern,this study constructs a new particle-scale microstructure model of weakly consolidated formation,and develop the corresponding methodology to simulate the sanding process and predict sand cavity shape.The microstructure model is a particle-objective model,which focuses on the random sedimentation of every sand grain.In the microstructure,every particle has its own size,sphericity and inclination angle.It is used to simulate the actual structure of cemented granular materials,which considers the heterogeneity and randomness of reservoir properties,provides the initial status for subsequent sanding simulation.With the particle detachment criteria,the microscopic simulation of sanding can be visually implemented to investigate the pattern and cavity shapes caused by sand production.The results indicate that sanding always starts initially from the borehole border,and then extends along the weakly consolidated plane,showing obvious characteristic of randomness.Three typical microscopic sanding patterns,concerning pore liquefaction,pseudo wormhole and continuous collapse,are proposed to illustrate the sanding mechanism in weakly consolidated reservoirs.The nonuniformity of sanding performance depends on the heterogeneous distribution of reservoir properties,such as rock strength and particle size.Finally,the three sanding patterns are verified by visually experimental work.The proposed integrated methodology is capable of predicting and describing the sanding cavity shape of an oil well after long-term sanding production,and providing the focus objective of future sand control measure.
基金the Research Grants Council(15220323)of the Hong Kong SAR,Chinathe Innovation Fund Denmark to SEM4Cities(IFD No.0143-0004)and RePUP(IFD No.2079-00030B)as well as the ARV project(EU H2020101036723).
文摘Efficient built environment control is essential for balancing energy consumption,thermal comfort,and indoor air quality(IAQ),especially in spaces with highly dynamic and intermittent occupancy patterns.Traditional control strategies,such as fixed schedules or simple occupancy-based rules,often fail to address the stochastic nature of occupancy behaviors,leading to suboptimal performance.This study proposes a stochastic occupancy-integrated model predictive control(MPC)strategy that advances built environment optimization through several innovative contributions.First,the proposed MPC integrates stochastic occupancy number predictions into its control scheme,enabling multi-objective optimization considering thermal comfort and IAQ for spaces with sudden occupancy changes and irregular usage.Second,the stochastic differential equations(SDE)-based building dynamic models are developed considering the stochasticity and time-inhomogeneity of occupancy heat gains and CO_(2)generations in the prediction of indoor temperature,CO_(2)concentration and energy consumption.Third,a TRNSYS-Python co-simulation platform is established to evaluate the MPC strategy’s performance,addressing the discrepancies between the SDE models used for MPC and the actual process of the target system.Finally,the study comprehensively evaluates the MPC’s multi-dimensional performance under different optimization weight combinations and benchmarks it against two baseline strategies:a fixed-schedule(FIX)strategy and occupancy-based control(OBC)strategies with varying per-person fresh airflow rates.Simulation results demonstrate that the proposed MPC achieves 32%energy savings and 17%IAQ improvement compared to the FIX strategy,and 30%thermal comfort improvement and 20%IAQ improvement with the same energy consumption compared to OBC.These findings highlight the robustness and enhanced performance of the proposed MPC in addressing the complexities of stochastic and time-varying occupancy,offering a state-of-the-art solution for energy-efficient and occupant-centric built environment control.
